Tool wear classification in precision machining using distance metrics and unsupervised machine learning

Debasish Mishra (), Utsav Awasthi (), Krishna R. Pattipati () and George M. Bollas ()
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Debasish Mishra: University of Connecticut
Utsav Awasthi: University of Connecticut
Krishna R. Pattipati: University of Connecticut
George M. Bollas: University of Connecticut

Journal of Intelligent Manufacturing, 2025, vol. 36, issue 1, No 18, 355 pages

Abstract: Abstract This article reports an unsupervised approach for estimation of the tool condition in precision machining processes. Three campaigns of run-to-failure experiments were conducted on different machines of varying capabilities to develop a generalized solution that is independent of machine intricacies and settings. The proposed approach uses real-time sensory information, such as vibration and spindle power, to infer the tool condition. The proposed approach utilizes distance metrics, Mahalanobis and Euclidean, determined from the sensory information as health indicators of tool wear, which are shown to be strongly correlated with the tool condition. The health indicators have a high correlation coefficient of 0.94 with tool wear measurements, across machines. Unsupervised approaches, such as Jenks Natural Breaks and K-means clustering, use these health indicators to estimate the tool condition. The developed unsupervised approach is also benchmarked using the IEEE PHM 2010 data. A Goodness of Variance Fit of 0.95 and 0.96 is achieved in classifying tool wear across the machining tests conducted in the three campaigns and IEEE PHM 2010 data, respectively. We highlight the explainability of the methods which improve the ease of deployment and engender trust.

Keywords: Precision machining; Machine learning; Tool condition monitoring; Mahalanobis distance; Euclidean distance; Jenks Natural Breaks; K-Means clustering; Decision Trees; IEEE PHM 2010 (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s10845-023-02239-5

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